class_docstr: Abstract class :class:`Optimiser` for all optimisers.
  
  It must be implemented by all subclasses in this package.
  
  Every :class:`Optimiser` instance can be run for one single step using the
  :py:meth:`run_step` method. The :class:`Optimiser` does not perform the
  evaluation of the objective function but only proposes values from its
  domain. Therefore an evaluation history must be supplied via the
  :py:meth`update` method. The history can be erased and the
  :class:`Optimiser` brought to the initial state via the :py:meth:`reset`
  method.
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docstr: Initialise the optimiser with a domain.
  
  Args:
      domain: :class:`Domain`. The domain of the objective function.
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code: def __init__(self, domain: Domain):
      """Initialise the optimiser with a domain.
  
      Args:
          domain: :class:`Domain`. The domain of the objective function.
      """
      self.domain = domain
      self._history: List[HistoryPoint] = []
  
file_path: hypertunity/hypertunity/optimisation/base.py
imports: import abc
  import math
  from dataclasses import dataclass
  from typing import Any, Dict, List, Sequence
  from hypertunity.domain import Domain, Sample
label_desc: Use image node_id 1 to create a new Optimiser object  with example: obj = Optimiser(domain)